Information Technology

When controlling dynamic systems, such as mobile
robots in uncertain environments, there is a trade off between
risk and reward. For example, a race car can turn
a corner faster by taking a more challenging path. This
paper proposes a new approach to planning a control sequence
with a guaranteed risk bound. Given a stochastic
dynamic model, the problem is to find a control sequence
that optimizes a performance metric, while satisfying
chance constraints i.e. constraints on the upper
bound of the probability of failure. We propose a
two-stage optimization approach, with the upper stage
optimizing the risk allocation and the lower stage calculating
the optimal control sequence that maximizes
reward. In general, the upper-stage is a non-convex optimization
problem, which is hard to solve. We develop
a new iterative algorithm for this stage that efficiently
computes the risk allocation with a small penalty to optimality. The algorithm is implemented and tested on
the autonomous underwater vehicle (AUV) depth planning
problem, and demonstrates a substantial improvement
in computation cost and suboptimality, compared
to the prior arts.

We present a novel method for information-theoretic exploration,
leveraging recent work on mapping and localization. We describe exploration as the constrained
optimization problem of computing a trajectory to minimize
posterior map error, subject to the constraints of
traveling through a set of sensing locations to ensure
map coverage. This trajectory is found by reducing the
map to a skeleton graph and searching for a minimum
entropy tour through the graph. We describe how a specific
factorization of the map covariance allows the reuse
of EKF updates during the optimization, giving an
efficient gradient ascent search for the maximum information
gain tour through sensing locations, where each
tour naturally incorporates revisiting well-known map
regions. By generating incrementally larger tours as the
exploration finds new regions of the environment, we
demonstrate that our approach can perform autonomous
exploration with improved accuracy.

With the aim of fluency and efficiency in human-robot teams,
we have developed a cognitive architecture based on the
neuropsychological principles of anticipation and perceptual
simulation through top-down biasing. An instantiation of
this architecture was implemented on a non-anthropomorphic
robotic lamp, performing in a human-robot collaborative task. In a human-subject study, in which the robot works on a
joint task with untrained subjects, we find our approach to be
significantly more efficient and fluent than in a comparable
system without anticipatory perceptual simulation. We also
show the robot and the human to be increasingly contributing
at a similar rate. Through self-report, we find significant
differences between the two conditions in the sense of team
fluency, the team's improvement over time, and the robot's
contribution to the efficiency and fluency. We also find difference
in verbal attitudes towards the robot: most notably,
subjects working with the anticipatory robot attribute more
positive and more human qualities to the robot, but display
increased self-blame and self-deprecation.

In order to interact successfully in social situations, a robot
must be able to observe others' actions and base its own behavior
on its beliefs about their intentions. Many interactions
take place in dynamic environments, and the outcomes of
people's or the robot's actions may be time-dependent. In
this paper, such interactions are modeled as a POMDP with
a time index as part of the state, resulting in a fully Markov
model with a potentially very large state space. The complexity
of finding even an approximate solution often limits
POMDP's practical applicability for large problems. This difficulty
is addressed through the development of an algorithm
for aggregating states in POMDPs with a time-indexed state
space. States that represent the same physical configuration
of the environment at different times are chosen to be combined
using reward-based metrics, preserving the structure of
the original model while producing a smaller model that is
faster to solve. We demonstrate that solving the aggregated
model produces a policy with performance comparable to the
policy from the original model. The example domains used
are a simulated elevator-riding task and a simulated driving
task based on data collected from human drivers.

We describe a novel integration of Planning with
Probabilistic State Estimation and Execution. The resulting
system is a unified representational and computational
framework based on declarative models and constraintbased
temporal plans. The work is motivated by the need to
explore the oceans more cost-effectively through the use of
Autonomous Underwater Vehicles (AUV), requiring them
to be goal-directed, perceptive, adaptive and robust in the
context of dynamic and uncertain conditions. The novelty of
our approach is in integrating deliberation and reaction over
different temporal and functional scopes within a single
model, and in breaking new ground in oceanography by
allowing for precise sampling within a feature of interest
using an autonomous robot. The system is general-purpose
and adaptable to other ocean going and terrestrial platforms.

How can we facilitate human-robot teamwork? The
teamwork literature has identified the need to know the
capabilities of teammates. How can we integrate the
knowledge of another agent's capabilities for a justifiably
intelligent teammate? This paper describes extensions to the
cognitive architecture, ACT-R, and the use of artificial
intelligence (AI) and cognitive science approaches to
produce a more cognitively-plausible, autonomous robotic
system that "mentally" simulates the decision-making of its
teammate. The extensions to ACT-R added capabilities to
interact with the real world through the robot's sensors and
effectors and simulate the decision-making of its teammate. The AI applications provided visual sensor capabilities by
methods clearly different than those used by humans. The
integration of these approaches into intelligent team-based
behavior is demonstrated on a mobile robot. Our
"TeamBot" matches the descriptive work and theories on
human teamwork. We illustrate our approach in a spatial,
team-oriented task of a guard force responding
appropriately to an alarm condition that requires the human
and robot team to "man" two guard stations as soon as
possible after the alarm.

Email client software is widely used for personal task
management, a purpose for which it was not designed and is
poorly suited. Past attempts to remedy the problem have
focused on adding task management features to the client UI. RADAR uses an alternative approach modeled on a trusted
human assistant who reads mail, identifies task-relevant
message content, and helps manage and execute tasks. This
paper describes the integration of diverse AI technologies
and presents results from human evaluation studies
comparing RADAR user performance to unaided COTS tool
users and users partnered with a human assistant. As
machine learning plays a central role in many system
components, we also compare versions of RADAR with and
without learning. Our tests show a clear advantage for
learning-enabled RADAR over all other test conditions.

We present a computational model, MoralDM, which
integrates several AI techniques in order to model recent
psychological findings on moral decision-making. Current
theories of moral decision-making extend beyond pure
utilitarian models by relying on contextual factors that vary
with culture. MoralDM uses a natural language system to
produce formal representations from psychological stimuli,
to reduce tailorability. The impacts of secular versus sacred
values are modeled via qualitative reasoning, using an order
of magnitude representation. MoralDM uses a combination
of first-principles reasoning and analogical reasoning to
determine consequences and utilities when making moral
judgments. We describe how MoralDM works and show
that it can model psychological results and improve its
performance via accumulating examples.

POIROT is an integration framework for combining machine
learning mechanisms to learn hierarchical models of
web services procedures from a single or very small set of
demonstration examples. The system is organized around a
shared representation language for communications with a
central hypothesis blackboard. Component learning systems
share semantic representations of their hypotheses (generalizations)
and inferences about demonstration traces. To
further the process, components may generate learning goals
for other learning components. POIROT's learners or hypothesis
formers develop workflows that include order dependencies,
subgoals, and decision criteria for selecting or
prioritizing subtasks and service parameters. Hypothesis
evaluators, guided by POIROT's meta-control component,
plan experiments to confirm or disconfirm hypotheses extracted
from these learning products. Collectively, they create
methods that POIROT can use to reproduce the demonstration
and solve similar problems. After its first phase of
development, POIROT has demonstrated it can learn some
moderately complex hierarchical task models from semantic
traces of user-generated service transaction sequences at a
level that is approaching human performance on the same
learning task.

Spatial scaffolding is a naturally occurring human teaching
behavior, in which teachers use their bodies to spatially structure
the learning environment to direct the attention of the
learner. Robotic systems can take advantage of simple, highly
reliable spatial scaffolding cues to learn from human teachers. We present an integrated robotic architecture that combines
social attention and machine learning components to
learn tasks effectively from natural spatial scaffolding interactions
with human teachers. We evaluate the performance of
this architecture in comparison to human learning data drawn
from a novel study of the use of embodied cues in human
task learning and teaching behavior. This evaluation provides
quantitative evidence for the utility of spatial scaffolding to
learning systems. In addition, this evaluation supported the
construction of a novel, interactive demonstration of a humanoid
robot taking advantage of spatial scaffolding cues to
learn from natural human teaching behavior.